The present disclosure relates generally to power generation systems. In particular, the present disclosure relates to controlling a power generation system through adaptive learning.
A power generation system (e.g., a gas turbine system) may be controlled by modeling the power generation system to estimate certain parameters of the power generation system. The estimated parameters may be used to facilitate efficient operation of the power generation system. For example, efficient scheduling, maintenance (which may shutdown the power generation system), operation settings (e.g., speed and/or time), and the like, may be determined using the estimated parameters.
A model of the power generation system may be generated based on inputs to the power generation system and correction or tuning factors. The model may be tuned in real-time such that a modeled output approximately matches a corresponding measured output via a correction factor (e.g., a multiplier applied to the model). However, the tuning may vary depending on differing ambient and/or operating conditions and include a delay due to a finite amount of response time. While this may be acceptable for slow condition changes, the delay may be significant for control during sufficiently fast transient events (e.g., when the power generation system changes its power output rapidly), resulting in reduced accuracy of the estimated parameters. This accuracy loss may lead to poor controllability of the power generation system and poor balancing of performance and life objectives.